
'''simple docstring'''






from typing import Any, Dict, List, Optional, Tuple, Union

import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler

from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transformers.models.fsmt.configuration_fsmt import FSMTConfig
from transformers.optimization import (
    Adafactor,
    AdamW,
    get_constant_schedule,
    get_constant_schedule_with_warmup,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.trainer_pt_utils import get_tpu_sampler
from transformers.training_args import ParallelMode
from transformers.utils import is_torch_tpu_available


if is_fairscale_available():
 from fairscale.optim import OSS


UpperCAmelCase_							: List[str] =							logging.get_logger(__name__)

UpperCAmelCase_							: Union[str, Any] =							{
    'linear': get_linear_schedule_with_warmup,
    'cosine': get_cosine_schedule_with_warmup,
    'cosine_w_restarts': get_cosine_with_hard_restarts_schedule_with_warmup,
    'polynomial': get_polynomial_decay_schedule_with_warmup,
    'constant': get_constant_schedule,
    'constant_w_warmup': get_constant_schedule_with_warmup,
}

class 			UpperCAmelCase__    (							A			):



    def __init__(						self   :							Tuple,__A   :							Any=None,__A   :							Dict=None,*__A   :							Tuple,**__A   :							List[Any]  ):
          super().__init__(*__A,**__A  )

          if config is None:
                assert isinstance(self.model,__A  ), (
                    "If no `config` is passed the model to be trained has to be of type `PreTrainedModel`, but is"
                    f' {self.model.__class__}'
                )
                _lowerCamelCase					: List[str]		    = self.model.config
          else:
                _lowerCamelCase					: Any		    = config

          _lowerCamelCase					: Union[str, Any]		    = data_args
          _lowerCamelCase					: int		    = self.config.tgt_vocab_size if isinstance(self.config,__A  ) else self.config.vocab_size

          if self.args.label_smoothing != 0 or (self.data_args is not None and self.data_args.ignore_pad_token_for_loss):
                assert self.config.pad_token_id is not None, (
                    "Make sure that `config.pad_token_id` is correcly defined when ignoring `pad_token` for loss"
                    " calculation or doing label smoothing."
                )

          if self.config.pad_token_id is None and self.config.eos_token_id is not None:
                logger.warning(
                    f'The `config.pad_token_id` is `None`. Using `config.eos_token_id` = {self.config.eos_token_id} for'
                    " padding.."  )

          if self.args.label_smoothing == 0:
                _lowerCamelCase					: Any		    = torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id  )
          else:
                # dynamically import label_smoothed_nll_loss
                from utils import label_smoothed_nll_loss

                _lowerCamelCase					: int		    = label_smoothed_nll_loss



    def 	lowerCamelCase_	(						self   :							Optional[int],__A   :							int  ):
          if self.optimizer is None:
                _lowerCamelCase					: Any		    = ["bias", "LayerNorm.weight"]
                _lowerCamelCase					: str		    = [
                    {
                        "params": [p for n, p in self.model.named_parameters() if not any(nd in n for nd in no_decay  )],
                        "weight_decay": self.args.weight_decay,
                    },
                    {
                        "params": [p for n, p in self.model.named_parameters() if any(nd in n for nd in no_decay  )],
                        "weight_decay": 0.0,
                    },
                ]
                _lowerCamelCase					: Union[str, Any]		    = Adafactor if self.args.adafactor else AdamW
                if self.args.adafactor:
                      _lowerCamelCase					: Any		    = Adafactor
                      _lowerCamelCase					: Any		    = {"scale_parameter": False, "relative_step": False}
                else:
                      _lowerCamelCase					: int		    = AdamW
                      _lowerCamelCase					: List[str]		    = {
                          "betas": (self.args.adam_betaa, self.args.adam_betaa),
                          "eps": self.args.adam_epsilon,
                      }
                _lowerCamelCase					: Union[str, Any]		    = self.args.learning_rate
                if self.sharded_ddp:
                      _lowerCamelCase					: List[str]		    = OSS(
                          params=__A,optim=__A,**__A,)
                else:
                      _lowerCamelCase					: str		    = optimizer_cls(__A,**__A  )

          if self.lr_scheduler is None:
                _lowerCamelCase					: List[str]		    = self._get_lr_scheduler(__A  )
          else:  # ignoring --lr_scheduler
                logger.warning("scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored."  )



    def 	lowerCamelCase_	(						self   :							Optional[int],__A   :							List[Any]  ):
          _lowerCamelCase					: Optional[Any]		    = arg_to_scheduler[self.args.lr_scheduler]
          if self.args.lr_scheduler == "constant":
                _lowerCamelCase					: int		    = schedule_func(self.optimizer  )
          elif self.args.lr_scheduler == "constant_w_warmup":
                _lowerCamelCase					: Optional[int]		    = schedule_func(self.optimizer,num_warmup_steps=self.args.warmup_steps  )
          else:
                _lowerCamelCase					: Tuple		    = schedule_func(
                    self.optimizer,num_warmup_steps=self.args.warmup_steps,num_training_steps=__A  )
          return scheduler



    def 	lowerCamelCase_	(						self   :							Union[str, Any]  ):
          if isinstance(self.train_dataset,torch.utils.data.IterableDataset  ):
                return None
          elif is_torch_tpu_available():
                return get_tpu_sampler(self.train_dataset  )
          else:
                if self.args.sortish_sampler:
                      self.train_dataset.make_sortish_sampler(
                          self.args.per_device_train_batch_size,distributed=(self.args.parallel_mode == ParallelMode.DISTRIBUTED),)

                return (
                    RandomSampler(self.train_dataset  )
                    if self.args.local_rank == -1
                    else DistributedSampler(self.train_dataset  )
                )



    def 	lowerCamelCase_	(						self   :							Tuple,__A   :							int,__A   :							Any,__A   :							Any  ):
          if self.args.label_smoothing == 0:
                if self.data_args is not None and self.data_args.ignore_pad_token_for_loss:
                      # force training to ignore pad token
                      _lowerCamelCase					: Optional[Any]		    = model(**__A,use_cache=__A  )[0]
                      _lowerCamelCase					: Optional[int]		    = self.loss_fn(logits.view(-1,logits.shape[-1]  ),labels.view(-1  )  )
                else:
                      # compute usual loss via models
                      _lowerCamelCase    ,		_lowerCamelCase					: str		    = model(**__A,labels=__A,use_cache=__A  )[:2]
          else:
                # compute label smoothed loss
                _lowerCamelCase					: int		    = model(**__A,use_cache=__A  )[0]
                _lowerCamelCase					: str		    = torch.nn.functional.log_softmax(__A,dim=-1  )
                _lowerCamelCase    ,		_lowerCamelCase					: Optional[int]		    = self.loss_fn(__A,__A,self.args.label_smoothing,ignore_index=self.config.pad_token_id  )
          return loss, logits



    def 	lowerCamelCase_	(						self   :							List[str],__A   :							int,__A   :							str  ):
          _lowerCamelCase					: List[Any]		    = inputs.pop("labels"  )
          _lowerCamelCase    ,		_lowerCamelCase					: int		    = self._compute_loss(__A,__A,__A  )
          return loss



    def 	lowerCamelCase_	(						self   :							Union[str, Any],__A   :							nn.Module,__A   :							Dict[str, Union[torch.Tensor, Any]],__A   :							bool,__A   :							Optional[List[str]] = None,):
          _lowerCamelCase					: List[str]		    = self._prepare_inputs(__A  )

          _lowerCamelCase					: Dict		    = {
              "max_length": self.data_args.val_max_target_length
              if self.data_args is not None
              else self.config.max_length,
              "num_beams": self.data_args.eval_beams if self.data_args is not None else self.config.num_beams,
          }

          if self.args.predict_with_generate and not self.args.prediction_loss_only:
                _lowerCamelCase					: Dict		    = self.model.generate(
                    inputs["input_ids"],attention_mask=inputs["attention_mask"],**__A,)
                # in case the batch is shorter than max length, the output should be padded
                if generated_tokens.shape[-1] < gen_kwargs["max_length"]:
                      _lowerCamelCase					: Dict		    = self._pad_tensors_to_max_len(__A,gen_kwargs["max_length"]  )

          _lowerCamelCase					: Optional[Any]		    = inputs.pop("labels"  )
          with torch.no_grad():
                # compute loss on predict data
                _lowerCamelCase    ,		_lowerCamelCase					: List[Any]		    = self._compute_loss(__A,__A,__A  )

          _lowerCamelCase					: List[Any]		    = loss.mean().detach()
          if self.args.prediction_loss_only:
                return (loss, None, None)

          _lowerCamelCase					: List[Any]		    = generated_tokens if self.args.predict_with_generate else logits

          if labels.shape[-1] < gen_kwargs["max_length"]:
                _lowerCamelCase					: Any		    = self._pad_tensors_to_max_len(__A,gen_kwargs["max_length"]  )

          return (loss, logits, labels)



    def 	lowerCamelCase_	(						self   :							int,__A   :							Dict,__A   :							int  ):
          # If PAD token is not defined at least EOS token has to be defined
          _lowerCamelCase					: List[Any]		    = self.config.pad_token_id if self.config.pad_token_id is not None else self.config.eos_token_id

          if pad_token_id is None:
                raise ValueError(
                    "Make sure that either `config.pad_token_id` or `config.eos_token_id` is defined if tensor has to be"
                    f' padded to `max_length`={max_length}'  )

          _lowerCamelCase					: Union[str, Any]		    = pad_token_id * torch.ones(
              (tensor.shape[0], max_length),dtype=tensor.dtype,device=tensor.device  )
          _lowerCamelCase					: Dict		    = tensor
          return padded_tensor

"""simple docstring"""


from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
      from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


SCREAMING_SNAKE_CASE			  =  logging.get_logger(__name__)

SCREAMING_SNAKE_CASE			  =  {
    """microsoft/deberta-v2-xlarge""": """https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json""",
    """microsoft/deberta-v2-xxlarge""": """https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json""",
    """microsoft/deberta-v2-xlarge-mnli""": (
        """https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json"""
    ),
    """microsoft/deberta-v2-xxlarge-mnli""": (
        """https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json"""
    ),
}



class 	__a	(   _lowerCAmelCase						):
       UpperCamelCase_		:       Any				=      '''deberta-v2'''






       def __init__(	self			:				Dict							,   UpperCAmelCase_			:				Optional[int]=128_100							,   UpperCAmelCase_			:				List[str]=1_536							,   UpperCAmelCase_			:				List[str]=24							,   UpperCAmelCase_			:				int=24							,   UpperCAmelCase_			:				int=6_144							,   UpperCAmelCase_			:				Optional[Any]="gelu"							,   UpperCAmelCase_			:				Tuple=0.1							,   UpperCAmelCase_			:				int=0.1							,   UpperCAmelCase_			:				Optional[int]=512							,   UpperCAmelCase_			:				Optional[int]=0							,   UpperCAmelCase_			:				Dict=0.02							,   UpperCAmelCase_			:				Optional[Any]=1e-7							,   UpperCAmelCase_			:				str=False							,   UpperCAmelCase_			:				Tuple=-1							,   UpperCAmelCase_			:				Optional[int]=0							,   UpperCAmelCase_			:				List[str]=True							,   UpperCAmelCase_			:				Any=None							,   UpperCAmelCase_			:				Optional[Any]=0							,   UpperCAmelCase_			:				Optional[Any]="gelu"							,   **UpperCAmelCase_			:				Optional[int]							,   )->	int:







              """simple docstring"""
              super().__init__(**UpperCAmelCase_      )

              UpperCamelCase       			=		hidden_size
              UpperCamelCase       			=		num_hidden_layers
              UpperCamelCase       			=		num_attention_heads
              UpperCamelCase       			=		intermediate_size
              UpperCamelCase       			=		hidden_act
              UpperCamelCase       			=		hidden_dropout_prob
              UpperCamelCase       			=		attention_probs_dropout_prob
              UpperCamelCase       			=		max_position_embeddings
              UpperCamelCase       			=		type_vocab_size
              UpperCamelCase       			=		initializer_range
              UpperCamelCase       			=		relative_attention
              UpperCamelCase       			=		max_relative_positions
              UpperCamelCase       			=		pad_token_id
              UpperCamelCase       			=		position_biased_input

              # Backwards compatibility
              if type(UpperCAmelCase_      ) == str:
                     UpperCamelCase       			=		[x.strip() for x in pos_att_type.lower().split("|"      )]

              UpperCamelCase       			=		pos_att_type
              UpperCamelCase       			=		vocab_size
              UpperCamelCase       			=		layer_norm_eps

              UpperCamelCase       			=		kwargs.get("pooler_hidden_size"							,   UpperCAmelCase_      )
              UpperCamelCase       			=		pooler_dropout
              UpperCamelCase       			=		pooler_hidden_act





class 	__a	(   _lowerCAmelCase						):






       @property
       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[Any]      )->	Mapping[str, Mapping[int, str]]:







              """simple docstring"""
              if self.task == "multiple-choice":
                     UpperCamelCase       			=		{0: "batch", 1: "choice", 2: "sequence"}
              else:
                     UpperCamelCase       			=		{0: "batch", 1: "sequence"}
              if self._config.type_vocab_size > 0:
                     return OrderedDict(
                         [("input_ids", dynamic_axis), ("attention_mask", dynamic_axis), ("token_type_ids", dynamic_axis)]      )
              else:
                     return OrderedDict([("input_ids", dynamic_axis), ("attention_mask", dynamic_axis)]      )






       @property
       def 						_SCREAMING_SNAKE_CASE							(	self			:				Union[str, Any]      )->	int:







              """simple docstring"""
              return 12






       def 						_SCREAMING_SNAKE_CASE							(	self			:				List[str]							,   UpperCAmelCase_			:				Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]							,   UpperCAmelCase_			:				int = -1							,   UpperCAmelCase_			:				int = -1							,   UpperCAmelCase_			:				int = -1							,   UpperCAmelCase_			:				bool = False							,   UpperCAmelCase_			:				Optional["TensorType"] = None							,   UpperCAmelCase_			:				int = 3							,   UpperCAmelCase_			:				int = 40							,   UpperCAmelCase_			:				int = 40							,   UpperCAmelCase_			:				"PreTrainedTokenizerBase" = None							,   )->	Mapping[str, Any]:







              """simple docstring"""
              UpperCamelCase       			=		super().generate_dummy_inputs(preprocessor=UpperCAmelCase_							,   framework=UpperCAmelCase_      )
              if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
                     del dummy_inputs["token_type_ids"]
              return dummy_inputs





